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Data-driven protein design and molecular latent space simulators
Data-driven modeling and deep learning present powerful tools that are opening up new paradigms and opportunities in the understanding, discovery, and design of soft and biological materials. I will describe our recent applications of deep representational learning to expose the sequence-function relationship within homologous protein families and to use these principles for the data-driven design and experimental testing of synthetic proteins with elevated function. I will then describe an approach based on latent space simulators to learn ultra-fast surrogate models of protein folding and biomolecular assembly by stacking three specialized deep learning networks to (i) encode a molecular system into a slow latent space, (ii) propagate dynamics in this latent space, and (iii) generatively decode a synthetic molecular trajectory.
Date:
5 May 2023, 14:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
Virtual
Speaker:
Prof Andrew Ferguson (University of Chicago)
Organising department:
Mathematical Institute
Organiser:
Sara Jolliffe (University of Oxford)
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Dr Peter Minary (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Sara Jolliffe
